Model Predictive Control Strategy for Residential Battery Energy Storage System in Volatile Electricity Market with Uncertain Daily Cycling Load  

作　　者：Dejan P.Jovanović Gerard F.Ledwich Geoffrey R.Walker 

机构地区：[1]Institute of Electrical and Electronic Engineers,Queensland,Australia [2]School of Electrical Engineering&Robotics,Queensland University of Technology,Brisbane,Australia

出　　处：《Journal of Modern Power Systems and Clean Energy》2023年第2期534-543,共10页现代电力系统与清洁能源学报（英文）

基　　金：supported by Australian Research Council (ARC)Discovery Project (No.160102571)。

摘　　要：This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.

关 键 词：Optimal control model predictive control(MPC) energy market nonlinear constrained optimization revenue for battery energy storage system Gaussian mixture model autoregressive integrated moving average model 

分 类 号：TM91[电气工程—电力电子与电力传动] TP273[自动化与计算机技术—检测技术与自动化装置]